Health research often involves multiple outcomes (e.g., survival, disease progression, quality-of-life scores). Traditional approaches analyse outcomes separately, implicitly treating outcomes as independent and potentially missing biological and temporal links. Alternatively, composite endpoints can be used but may hide heterogeneity in clinical importance, frequency, and treatment response. As a result, both predictive performance and clinical interpretability may be compromised.
Compare strategies for both clinical trial design and prediction modelling that treat outcomes independently, as composite endpoints, and via hierarchical/multi-state frameworks (with potential extension to multi-task modelling).
This data set is used for both clinical trial and predictive modelling. In this clinical trial, the three treatment arms were considered as different combinations of first-line treatment strategies with a primary otucome of Overall Survival and secondary outcomes included Progression-Free Survival.
Preliminary time-to-event analyses was conducted for Overall Survival (OS) and Progression-Free Survival (PFS) separately. Survival distributions are visualised using Kaplan–Meier plots. There was no evidence of a difference in OS between three treatment arms (log-rank test : chi-squared = 2.8, p-value = 0.20). Cox proportional hazards models are fitted to estimate covariate-adjusted effects, and as a preliminary predictive modelling strategy.
Results are interpreted in both clinical and methodological contexts, highlighting the limitations of analysing multiple outcomes independently and motivating integrated modelling approaches.
| A (N=69) |
B (N=69) |
C (N=68) |
|
|---|---|---|---|
| Sites | |||
| 1 - 2 | 43 (62.3%) | 40 (58.0%) | 42 (61.8%) |
| >= 3 | 26 (37.7%) | 29 (42.0%) | 26 (38.2%) |
| LDH | |||
| Normal | 41 (59.4%) | 39 (56.5%) | 47 (69.1%) |
| Elevated | 26 (37.7%) | 28 (40.6%) | 20 (29.4%) |
| Missing | 2 (2.9%) | 2 (2.9%) | 1 (1.5%) |
| TMB | |||
| < 10 | 20 (29.0%) | 17 (24.6%) | 18 (26.5%) |
| >= 10 | 8 (11.6%) | 8 (11.6%) | 12 (17.6%) |
| Missing | 41 (59.4%) | 44 (63.8%) | 38 (55.9%) |
| JAK | |||
| Wild Type (Normal) | 24 (34.8%) | 17 (24.6%) | 16 (23.5%) |
| Mutated | 5 (7.2%) | 7 (10.1%) | 14 (20.6%) |
| Missing | 40 (58.0%) | 45 (65.2%) | 38 (55.9%) |
| Variable | HR (95% CI) | p-value |
|---|---|---|
| ArmB | 0.98 (0.38–2.50) | 0.9590 |
| ArmC | 0.83 (0.33–2.07) | 0.6900 |
| Sites>= 3 | 2.02 (0.93–4.42) | 0.0764 |
| LDHElevated | 0.96 (0.43–2.15) | 0.9130 |
| TMB>= 10 | 0.72 (0.31–1.67) | 0.4480 |
| JAKMutated | 0.63 (0.24–1.63) | 0.3380 |
| Variable | HR (95% CI) | p-value |
|---|---|---|
| ArmB | 0.73 (0.30–1.77) | 0.4870 |
| ArmC | 1.03 (0.48–2.22) | 0.9470 |
| Sites>= 3 | 1.60 (0.80–3.20) | 0.1870 |
| LDHElevated | 1.14 (0.56–2.34) | 0.7150 |
| TMB>= 10 | 0.85 (0.40–1.78) | 0.6660 |
| JAKMutated | 0.41 (0.16–1.03) | 0.0571 |
| Endpoint | Chi.square | df | p.value |
|---|---|---|---|
| OS | 2.80 | 2 | 0.2460 |
| PFS | 7.74 | 2 | 0.0208 |
| Endpoint | PH.test.p.value |
|---|---|
| OS | 0.0581 |
| PFS | 0.0119 |
The code and datasets for this project can be viewed at our GitHub repository here: https://github.com/darshu-d/MSc-Research-project-
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